发表机构
Duke University（杜克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出路径-流对齐统一训练目标，联合训练端点保持路径网络与流网络，识别路径过拟合失效模式并引入随机路径正则化器抑制低熵瓶颈，在ImageNet-256x256上持续改善FID。
AI 中文摘要
我们研究路径-流对齐作为流匹配的统一训练目标。不同于固定插值路径并仅学习速度场，我们使用相同的对齐损失联合训练一个保持端点的路径网络和一个流网络：流学习匹配路径速度，而路径学习将其速度与当前流对齐。尽管每个固定的学习路径都定义了一个有效的流匹配目标，但单独的对齐损失并不是路径学习的可靠标准。我们识别出路径过拟合这一失效模式，在该模式下对齐损失下降而样本质量恶化。我们发现这种失效与诱导概率路径中的低熵瓶颈相关，即学习到的路径将样本路由通过过度集中的中间边际分布。基于这一诊断，我们引入一种随机路径正则化器，该正则化器在保持精确端点的同时，向路径网络隐藏部分源信息。由此产生的正则化为随机训练路径边际分布提供了显式的熵下限，并在经验上抑制了学习采样器中的瓶颈，使得联合路径-流训练变得有效。在ImageNet-256x256上使用SiT骨干网络时，我们的方法在不同模型规模上持续改进FID，扩展到模型引导训练，并保持推理时的架构和采样器不变。代码可在以下网址获取：此https URL
英文摘要
We study path-flow alignment as a unified training objective for flow matching. Instead of fixing the interpolation path and learning only the velocity field, we jointly train an endpoint-preserving path network and a flow network using the same alignment loss: the flow learns to match the path velocity, and the path learns to align its velocity to the current flow. Although every fixed learned path defines a valid flow-matching objective, the alignment loss alone is not a reliable criterion for path learning. We identify path overfitting, a failure mode in which the alignment loss decreases while sample quality worsens. We find that this failure is associated with low-entropy bottlenecks in the induced probability path, where the learned path routes samples through overly concentrated intermediate marginals. Motivated by this diagnosis, we introduce a stochastic path regularizer that hides part of the source information from the path network while preserving exact endpoints. The resulting regularization gives an explicit entropy floor for the stochastic training-path marginals and empirically suppresses the bottleneck in the learned sampler, making joint path-flow training effective. On ImageNet-256x256 with SiT backbones, our method consistently improves FID across model scales, extends to model-guidance training, and leaves the inference-time architecture and sampler unchanged. Code is available at https://github.com/lizeyu090312/traj_opt_paper
